Update by Ali

[ThesisAli.git] / CHAPITRE_05.tex

diff --git a/CHAPITRE_05.tex b/CHAPITRE_05.tex
index 5a39fd7049beed870d4b961bc854c9c0d7b671c7..918ec9055139bbe4671dd34a326ba3b58b7266ac 100644 (file)
--- a/CHAPITRE_05.tex
+++ b/CHAPITRE_05.tex
@@ -41,11 +41,6 @@ mechanisms: subdividing the area of interest into several subregions (like a clu
 
 As can be seen in Figure~\ref{fig2},  our protocol  works in  periods fashion, where  each is  divided  into 4  phases: Information~Exchange,  Leader~Election, Decision, and Sensing. 
 %The information exchange among wireless sensor nodes is described in chapter 4, section \ref{ch4:sec:02:03:01}. The leader election in each subregion is explained in chapter 4, section \ref{ch4:sec:02:03:02}, 
 
 As can be seen in Figure~\ref{fig2},  our protocol  works in  periods fashion, where  each is  divided  into 4  phases: Information~Exchange,  Leader~Election, Decision, and Sensing. 
 %The information exchange among wireless sensor nodes is described in chapter 4, section \ref{ch4:sec:02:03:01}. The leader election in each subregion is explained in chapter 4, section \ref{ch4:sec:02:03:02}, 

-The difference with MuDiLCO in that the elected leader in each subregion is for each period. In the decision phase, each leader will solve an integer  program to select which  cover sets  will be activated in  the following  sensing phase  to cover the  subregion to  which it belongs.  The integer  program will produce $T$ cover sets,  one for each round. The leader will send an ActiveSleep  packet to each sensor in the subregion based on the algorithm's results, indicating if the sensor should be active or not in
-each round  of the  sensing phase. Each sensing phase is itself divided into $T$ rounds and for each round a set of sensors (a cover set) is responsible for the sensing task. 
-%Each sensor node in the subregion will receive an ActiveSleep packet from leader, informing it to stay awake or to go to sleep for  each round of the sensing  phase. 
-Algorithm~\ref{alg:MuDiLCO}, which will be  executed by each node at the beginning  of a period, explains  how the ActiveSleep packet is obtained. In this way, a multiround optimization  process is performed  during each
-period  after  Information~Exchange  and  Leader~Election phases,  in  order to produce $T$ cover sets that will take the mission of sensing for $T$ rounds.

 \begin{figure}[ht!]
 \centering \includegraphics[width=160mm]{Figures/ch5/GeneralModel.jpg} % 70mm  Modelgeneral.pdf
 \caption{MuDiLCO protocol.}
 \begin{figure}[ht!]
 \centering \includegraphics[width=160mm]{Figures/ch5/GeneralModel.jpg} % 70mm  Modelgeneral.pdf
 \caption{MuDiLCO protocol.}


@@ -53,12 +48,6 @@ period  after  Information~Exchange  and  Leader~Election phases,  in  order to
 \end{figure} 
 
 
 \end{figure} 
 
 

-%This protocol minimizes the impact of unexpected node failure (not due to batteries running out of energy), because it works in periods. On the one hand, if a node failure is detected before making the decision, the node will not participate during this phase. On the other hand, if the node failure occurs after the decision, the sensing  task of the network will be temporarily affected:  only during  the period of sensing until a new period starts.
-
-%The  energy consumption  and some other constraints  can easily  be  taken into account since the  sensors  can  update and  then  exchange their  information (including their residual energy) at the beginning of each period.  However, the pre-sensing  phases (Information  Exchange, Leader  Election, and  Decision) are energy  consuming for some  nodes, even  when they  do not  join the  network to monitor the area.
-
- 
-

 \begin{algorithm}[h!]                
  % \KwIn{all the parameters related to information exchange}
 %  \KwOut{$winer-node$ (: the id of the winner sensor node, which is the leader of current round)}
 \begin{algorithm}[h!]                
  % \KwIn{all the parameters related to information exchange}
 %  \KwOut{$winer-node$ (: the id of the winner sensor node, which is the leader of current round)}


@@ -99,9 +88,24 @@ period  after  Information~Exchange  and  Leader~Election phases,  in  order to
 
 \end{algorithm}
 
 
 \end{algorithm}
 

+The difference with MuDiLCO in that the elected leader in each subregion is for each period. In the decision phase, each leader will solve an integer  program to select which  cover sets  will be activated in  the following  sensing phase  to cover the  subregion to  which it belongs.  The integer  program will produce $T$ cover sets,  one for each round. The leader will send an ActiveSleep  packet to each sensor in the subregion based on the algorithm's results, indicating if the sensor should be active or not in
+each round  of the  sensing phase. Each sensing phase is itself divided into $T$ rounds and for each round a set of sensors (a cover set) is responsible for the sensing task. 
+%Each sensor node in the subregion will receive an ActiveSleep packet from leader, informing it to stay awake or to go to sleep for  each round of the sensing  phase. 
+Algorithm~\ref{alg:MuDiLCO}, which will be  executed by each node at the beginning  of a period, explains  how the ActiveSleep packet is obtained. In this way, a multiround optimization  process is performed  during each
+period  after  Information~Exchange  and  Leader~Election phases,  in  order to produce $T$ cover sets that will take the mission of sensing for $T$ rounds. The flowchart of MuDiLCO protocol executed in each sensor node is presented in Figure \ref{flow5}.

 
 

+\begin{figure}[ht!]
+\centering
+\includegraphics[scale=0.50]{Figures/ch5/Algo2.png} % 70mm
+\caption{The flowchart of MuDiLCO protocol.}
+\label{flow5}
+\end{figure} 

 
 
 
 

+%This protocol minimizes the impact of unexpected node failure (not due to batteries running out of energy), because it works in periods. On the one hand, if a node failure is detected before making the decision, the node will not participate during this phase. On the other hand, if the node failure occurs after the decision, the sensing  task of the network will be temporarily affected:  only during  the period of sensing until a new period starts.
+
+%The  energy consumption  and some other constraints  can easily  be  taken into account since the  sensors  can  update and  then  exchange their  information (including their residual energy) at the beginning of each period.  However, the pre-sensing  phases (Information  Exchange, Leader  Election, and  Decision) are energy  consuming for some  nodes, even  when they  do not  join the  network to monitor the area.
+

 \section{Primary Points based Multiround Coverage Problem Formulation}
 \label{ch5:sec:03}
 
 \section{Primary Points based Multiround Coverage Problem Formulation}
 \label{ch5:sec:03}
 


@@ -112,6 +116,8 @@ period  after  Information~Exchange  and  Leader~Election phases,  in  order to
 
 We extend the mathematical formulation given in section \ref{ch4:sec:03} to take into account multiple rounds.
 
 
 We extend the mathematical formulation given in section \ref{ch4:sec:03} to take into account multiple rounds.
 

+\newpage
+

 For a  primary point  $p$, let $\alpha_{j,p}$  denote the indicator  function of
 whether the point $p$ is covered, that is
 \begin{equation}
 For a  primary point  $p$, let $\alpha_{j,p}$  denote the indicator  function of
 whether the point $p$ is covered, that is
 \begin{equation}


@@ -331,8 +337,8 @@ Obviously, in  that case, DESK and GAF have fewer active nodes since they have a
 %\label{ch5:sec:03:02:03}
 
 Figure~\ref{fig6} reports the cumulative  percentage of stopped simulations runs per round for 150 deployed nodes. This figure gives the  breakpoint for each method.  
 %\label{ch5:sec:03:02:03}
 
 Figure~\ref{fig6} reports the cumulative  percentage of stopped simulations runs per round for 150 deployed nodes. This figure gives the  breakpoint for each method.  

-DESK stops first,  after approximately 45~rounds, because it consumes the more energy by  turning on a large number of redundant  nodes during the sensing phase. GAF  stops secondly for the  same reason than  DESK.  MuDiLCO overcomes DESK and GAF because the  optimization process distributed on several subregions leads  to coverage  preservation and  so extends  the network  lifetime.  Let us
-emphasize that the  simulation continues as long as a network  in a subregion is still connected. \\
+DESK stops first,  after approximately 45~rounds, because it consumes the more energy by  turning on a large number of redundant  nodes during the sensing phase. GAF  stops secondly for the  same reason than  DESK. \\\\\\ MuDiLCO overcomes DESK and GAF because the  optimization process distributed on several subregions leads  to coverage  preservation and  so extends  the network  lifetime.  Let us
+emphasize that the  simulation continues as long as a network  in a subregion is still connected. 

 
 
 \begin{figure}[t]
 
 
 \begin{figure}[t]


@@ -379,7 +385,7 @@ the number of  sensors involved in the integer program, the larger the time comp
 
 We observe  the impact of the  network size and of  the number of  rounds on the
 computation  time.   Figure~\ref{fig77} gives  the  average  execution times  in
 
 We observe  the impact of the  network size and of  the number of  rounds on the
 computation  time.   Figure~\ref{fig77} gives  the  average  execution times  in

-seconds (needed to solve optimization problem) for different values of $T$. The original execution time is computed as described in chapter 4, section \ref{ch4:sec:04:02}.
+seconds (needed to solve optimization problem) for different values of $T$. \\\\\\ 

 
 %The original execution time  is computed on a laptop  DELL with Intel Core~i3~2370~M (2.4 GHz)  processor (2  cores) and the  MIPS (Million Instructions  Per Second) rate equal to 35330. To be consistent  with the use of a sensor node with Atmels AVR ATmega103L  microcontroller (6 MHz) and  a MIPS rate  equal to 6 to  run the optimization   resolution,   this  time   is   multiplied   by  2944.2   $\left( \frac{35330}{2} \times  \frac{1}{6} \right)$ and  reported on Figure~\ref{fig77} for different network sizes.
 
 
 %The original execution time  is computed on a laptop  DELL with Intel Core~i3~2370~M (2.4 GHz)  processor (2  cores) and the  MIPS (Million Instructions  Per Second) rate equal to 35330. To be consistent  with the use of a sensor node with Atmels AVR ATmega103L  microcontroller (6 MHz) and  a MIPS rate  equal to 6 to  run the optimization   resolution,   this  time   is   multiplied   by  2944.2   $\left( \frac{35330}{2} \times  \frac{1}{6} \right)$ and  reported on Figure~\ref{fig77} for different network sizes.
 


@@ -390,10 +396,10 @@ seconds (needed to solve optimization problem) for different values of $T$. The
 \label{fig77}
 \end{figure} 
 
 \label{fig77}
 \end{figure} 
 

-As expected,  the execution time increases  with the number of  rounds $T$ taken into account to schedule the sensing phase. The times obtained for $T=1,3$ or $5$ seem bearable, but for $T=7$ they become quickly unsuitable for a sensor node, especially when  the sensor network size increases.   Again, we can notice that if we want  to schedule the nodes activities for a  large number of rounds,
+The original execution time is computed as described in chapter 4, section \ref{ch4:sec:04:02}. As expected,  the execution time increases  with the number of  rounds $T$ taken into account to schedule the sensing phase. The times obtained for $T=1,3$ or $5$ seem bearable, but for $T=7$ they become quickly unsuitable for a sensor node, especially when  the sensor network size increases.   Again, we can notice that if we want  to schedule the nodes activities for a  large number of rounds,

 we need to choose a relevant number of subregions in order to avoid a complicated and cumbersome optimization.  
 
 we need to choose a relevant number of subregions in order to avoid a complicated and cumbersome optimization.  
 

-On the one hand, a large value  for $T$ permits to reduce the  energy overhead due  to the three  pre-sensing phases, on  the other hand  a leader  node may  waste a  considerable amount  of energy  to  solve the optimization problem. \\ \\ \\ \\ \\ \\ \\
+On the one hand, a large value  for $T$ permits to reduce the  energy overhead due  to the three  pre-sensing phases, on  the other hand  a leader  node may  waste a  considerable amount  of energy  to  solve the optimization problem. %\\ \\ \\ \\ \\ \\ \\

 
 \item {{\bf Network lifetime}}
 %\subsection{Network lifetime}
 
 \item {{\bf Network lifetime}}
 %\subsection{Network lifetime}


@@ -416,7 +422,7 @@ protocol  maximizes the  lifetime of  the network.   In particular,  the  gain i
 
 
 The  slight decrease that can be observed  for MuDiLCO-7 in case of  $Lifetime_{95}$  with  large  wireless  sensor  networks  results  from  the difficulty  of the optimization  problem to  be solved  by the  integer program.
 
 
 The  slight decrease that can be observed  for MuDiLCO-7 in case of  $Lifetime_{95}$  with  large  wireless  sensor  networks  results  from  the difficulty  of the optimization  problem to  be solved  by the  integer program.

-This  point was  already noticed  in \ref{subsec:EC} devoted  to the
+\\\\\\\\This  point was  already noticed  in \ref{subsec:EC} devoted  to the

 energy consumption,  since network lifetime and energy  consumption are directly linked.
 \end{enumerate} 
 
 energy consumption,  since network lifetime and energy  consumption are directly linked.
 \end{enumerate} 
 


@@ -430,4 +436,4 @@ In this chapter, we have presented a protocol, called MuDiLCO (Multiround  Distr
 Simulations results show the  relevance of the proposed  protocol in  terms of lifetime, coverage  ratio, active  sensors ratio, energy  consumption, execution time. Indeed,  when dealing with  large wireless sensor networks,  a distributed approach, like  the one we  propose, allows to reduce the difficulty of  a single global optimization problem by partitioning it into many smaller problems, one per subregion, that can be solved  more easily. Nevertheless, results also show that it is not possible to plan the activity of sensors over too many rounds because the resulting optimization problem leads to too high-resolution times and thus to an excessive energy consumption. Compared with DiLCO, it is clear that MuDiLCO improves the network lifetime especially for the dense network, but it is  less robust than DiLCO under sensor nodes failures. Therefore, choosing the number of rounds $T$ depends on the type of application the WSN is deployed for. 
 
 
 Simulations results show the  relevance of the proposed  protocol in  terms of lifetime, coverage  ratio, active  sensors ratio, energy  consumption, execution time. Indeed,  when dealing with  large wireless sensor networks,  a distributed approach, like  the one we  propose, allows to reduce the difficulty of  a single global optimization problem by partitioning it into many smaller problems, one per subregion, that can be solved  more easily. Nevertheless, results also show that it is not possible to plan the activity of sensors over too many rounds because the resulting optimization problem leads to too high-resolution times and thus to an excessive energy consumption. Compared with DiLCO, it is clear that MuDiLCO improves the network lifetime especially for the dense network, but it is  less robust than DiLCO under sensor nodes failures. Therefore, choosing the number of rounds $T$ depends on the type of application the WSN is deployed for. 
 
 

- 
\ No newline at end of file
+